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      Synergies and tradeoffs between cash crop production and food security: a case study in rural Ghana

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          Estimating wealth effects without expenditure data--or tears: an application to educational enrollments in states of India.

          Using data from India, we estimate the relationship between household wealth and children's school enrollment. We proxy wealth by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights. In Indian data this index is robust to the assets included, and produces internally coherent results. State-level results correspond well to independent data on per capita output and poverty. To validate the method and to show that the asset index predicts enrollments as accurately as expenditures, or more so, we use data sets from Indonesia, Pakistan, and Nepal that contain information on both expenditures and assets. The results show large, variable wealth gaps in children's enrollment across Indian states. On average a "rich" child is 31 percentage points more likely to be enrolled than a "poor" child, but this gap varies from only 4.6 percentage points in Kerala to 38.2 in Uttar Pradesh and 42.6 in Bihar.
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            Food variety and dietary diversity scores in children: are they good indicators of dietary adequacy?

            To assess whether a food variety score (FVS) and/or a dietary diversity score (DDS) are good indicators of nutrient adequacy of the diet of South African children. Secondary data analyses were undertaken with nationally representative data of 1-8-year-old children (n = 2200) studied in the National Food Consumption Study in 1999. An average FVS (mean number of different food items consumed from all possible items eaten) and DDS (mean number of food groups out of nine possible groups) were calculated. A nutrient adequacy ratio (NAR) is the ratio of a subject's nutrient intake to the estimated average requirement calculated using the Food and Agriculture Organization/World Health Organization (2002) recommended nutrient intakes for children. The mean adequacy ratio (MAR) was calculated as the sum of NARs for all evaluated nutrients divided by the number of nutrients evaluated, expressed as a percentage. MAR was used as a composite indicator for micronutrient adequacy. Pearson correlation coefficients between FVS, DDS and MAR were calculated and also evaluated for sensitivity and specificity, with MAR taken as the ideal standard of adequate intake. The relationships between MAR and DDS and between anthropometric Z-scores and DDS were also evaluated. The children had a mean FVS of 5.5 (standard deviation (SD) 2.5) and a mean DDS of 3.6 (SD 1.4). The mean MAR (ideal = 100%) was 50%, and was lowest (45%) in the 7-8-year-old group. The items with the highest frequency of consumption were from the cereal, roots and tuber group (99.6%), followed by the 'other group' (87.6%) comprising items such as tea, sugar, jam and sweets. The dairy group was consumed by 55.8%, meat group by 54.1%, fats by 38.9%, other vegetables by 30.8%, vitamin-A-rich by 23.8%, other fruit by 22%, legumes and nuts by 19.7% and eggs by 13.3%. There was a high correlation between MAR and both FVS (r = 0.726; P < 0.0001) and DDS (r = 0.657; P < 0.0001), indicating that either FVS or DDS can be used as an indicator of the micronutrient adequacy of the diet. Furthermore, MAR, DDS and FVS showed significant correlations with height-for-age and weight-for-age Z-scores, indicating a strong relationship between dietary diversity and indicators of child growth. A DDS of 4 and an FVS of 6 were shown to be the best indicators of MAR less than 50%, since they provided the best sensitivity and specificity. Either FVS or DDS can be used as a simple and quick indicator of the micronutrient adequacy of the diet.
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              The World Health Organization Global Database on Child Growth and Malnutrition: methodology and applications.

              For decades nutritional surveys have been conducted using various definitions, indicators and reference populations to classify child malnutrition. The World Health Organization (WHO) Global Database on Child Growth and Malnutrition was initiated in 1986 with the objective to collect, standardize, and disseminate child anthropometric data using a standard format. The database includes population-based surveys that fulfil a set of criteria. Data are checked for validity and consistency and raw data sets are analysed following a standard procedure to obtain comparable results. Prevalences of wasting, stunting, under- and overweight in preschool children are presented using z-scores based on the National Center for Health Statistics (NCHS)/WHO international reference population. New surveys are included on a continuous basis and updates are published bimonthly on the database's web site. To date, the database contains child anthropometric information derived from 846 surveys. With 412 national surveys from 138 countries and 434 sub-national surveys from 155 countries, the database covers 99% and 64% of the under 5 year olds in developing and developed countries, respectively. This wealth of information enables international comparison of nutritional data, helps identifying populations in need, evaluating nutritional and other public health interventions, monitoring trends in child growth, and raising political awareness of nutritional problems. The 15 years experience of the database can be regarded as a success story of international collaboration in standardizing child growth data. We recommend this model for monitoring other nutritional health conditions that as yet lack comparable data.
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                Author and article information

                Journal
                Food Security
                Food Sec.
                Springer Science and Business Media LLC
                1876-4517
                1876-4525
                August 2014
                July 6 2014
                August 2014
                : 6
                : 4
                : 541-554
                Article
                10.1007/s12571-014-0360-6
                1d331845-1bf1-4c34-a077-cc1c5c95d8ef
                © 2014
                History

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